A Clustering Approach for Predicting Dune Morphodynamic Response to Storms Using Typological Coastal Profiles

A Clustering Approach for Predicting Dune Morphodynamic Response to Storms Using Typological Coastal Profiles: A Case Study at the Dutch Coast

Athanasiou, Panagiotis (Deltares; University of Twente)
Van Dongeren, Ap (Deltares; IHE Delft Institute for Water Education)
Giardino, Alessio (Deltares; Asian Development Bank)
Vousdoukas, Michalis (Joint Research Centre)
Antolínez, José A. Á. (TU Delft Coastal Engineering)
Ranasinghe, Roshanka (University of Twente; Deltares; IHE Delft Institute for Water Education)

2021

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.

clustering
data mining
dune erosion
Dutch coast
K-means
XBeach

http://resolver.tudelft.nl/uuid:044d5c44-fdca-485f-9c51-9e1518867f7e

https://doi.org/10.3389/fmars.2021.747754

2296-7745

Frontiers in Marine Science, 8, 1-20

Institutional Repository

journal article

© 2021 Panagiotis Athanasiou, Ap Van Dongeren, Alessio Giardino, Michalis Vousdoukas, José A. Á. Antolínez, Roshanka Ranasinghe